Temporal Variability Of Rainfall Research Articles

Advantages of GSMaP Data for Multi‐Timescale Precipitation Estimation in Luzon

AbstractThe Global Precipitation Measurement Mission (GPM) provides two different sources of post‐real‐time satellite‐based rainfall estimates, including the Integrated Multi‐satellitE Retrievals for GPM Final Run (herein IMERG‐F) and Global Satellite Mapping of Precipitation‐Gauge (herein GSMaP‐G). However, relative to IMERG‐F, GSMaP‐G has been less thoroughly evaluated in the context of studying rainfall variations over Luzon, Philippines. Using rain‐gauge observations over Luzon as a reference base, this study aimed to clarify if GSMaP‐G v07 is more capable than IMERG‐F v06 in regard to representing rainfall variations over Luzon at multiple timescales (including intraseasonal, annual, and interannual). The results revealed that both IMERG‐F and GSMaP‐G performed better in regard to depicting the temporal phase evolution for the wet season rainfall than they did for the dry season rainfall for Luzon. However, relative to IMERG‐F, GSMaP‐G exhibited better performance in most examined features, including (a) the detection of temporal variations (both phase and amplitude) of rainfall on the intraseasonal, annual, and interannual timescales; (b) the representation of spatial differences with more rainfall occurring from April to September for western Luzon and from October to March for eastern Luzon; (c) the detection of occurrence of rainfall events at various intensities. The analysis also demonstrates that the difference between the performance of IMERG‐F and GSMaP‐G in regard to quantitative rainfall estimation is larger during the wet season than it is during the dry season. The possible cause for their performance differences over Luzon is the differences in the gauge‐adjustment method and the biases in its pre‐non‐gauge‐adjusted product.

A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network

Accurate quantitative precipitation estimation (QPE) utilizing radar data plays a vital role in weather monitoring, hydrological modelling and prediction. In previous research, some regression techniques such as the reflectivity (Z) - rainfall (R) relationship and artificial intelligence methods were employed for radar QPE; however, the estimation results remain limited the spatial and temporal variability of rainfall results in large errors in radar QPE. Recently, an emerging deep learning approach that can be applied in hydrological areas for QPE, namely, the gated recurrent unit (GRU) network method, has been used. GRU networks have evolved from long short-term memory (LSTM) networks, with improved training speed and modelling accuracy. In this research, the GRU network is applied to QPE using the three-dimensional structure of reflectivity from the Constant Altitude Plan Position Indicator (3D CAPPI) for an urban catchment in Seoul, South Korea. Simultaneously, a “complete” 3D CAPPI based on a cubic grid of 4 × 4 km2 that is highly suitable for the study area is proposed. Based on the comparison of the results of the GRU model with those of the artificial neural networks (ANNs), recurrent neural networks (RNNs), and LSTM networks, and the Z-R relationship, namely the least-squares model (LSM) for 2 testing strategies including traditional test strategy and leave-one-out cross-validation (LOOCV) strategy, the results indicate that the GRU model is superior to other models in radar QPE, with improvements by 3.3 ∼ 46.2%, 25 ∼ 70% and 1.2 ∼ 44.8% in the root mean square error (RMSE), bias and correlation coefficient (CC). This study presents a valuable method for improving the accuracy of radar QPE that can be actively employed in the future.

A review of video‐based rainfall measurement methods

AbstractAccurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video‐based rainfall measurement, a novel method, has the advantages of real‐time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video‐based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video‐based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning‐based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video‐based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks.This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes

Temporal variations in rainfall, runoff and sediment yield in small karst watersheds

As one of the largest and most typical continuous karst distribution belts in the world, Southwest China is vulnerable to soil erosion and rocky desertification because of its unique geological conditions and heterogeneous landscapes. Understanding the rainfall-runoff-sediment transport relationships is critical to effectively controlling soil erosion in karst watersheds. This study aims to evaluate the sediment yield variability at different temporal scales (daily, monthly/seasonal, and annual) from 2014 to 2019 and to further identify the rainfall and runoff variables most associated with sediment yield in the selected Mahuangtian (2.26 km2) and Muzhaihe (0.09 km2) karst watersheds. Since rainfall and runoff variables were highly codependent, the partial least squares regression (PLSR) model was used to quantify the relative importance of rainfall and runoff variables to sediment yield. The variable importance of the projection (VIP) scores>1 was considered to be the most influential predictor of the PLSR model. Results showed that sediment yield was greatly influenced by rainfall and runoff, which can explain 70% and 85% of the variation of sediment yield in the Mahuangtian and Muzhaihe watersheds, respectively. For rainfall variables, three-day antecedent precipitation (VIP = 1.41) and five-day antecedent precipitation (VIP = 1.28) were the main first-order factors controlling sediment yield in the Mahuangtian watershed. In contrast, the rainfall amount (VIP = 1.35) was the dominant factor influencing sediment yield in the Muzhaihe watershed. For runoff variables, runoff amount, runoff coefficient, and maximum discharge were the primary variables controlling the sediment yield in these two karst watersheds. By partially eliminating the co-dependency of the variables, the PLSR approach can conveniently determine the dominant factors controlling sediment yield in heterogeneous karst watersheds.

Bartlett–Lewis Model Calibrated with Satellite-Derived Precipitation Data to Estimate Daily Peak 15 Min Rainfall Intensity

Temporal variability of rainfall is extreme in the rangelands of northern Australia and occurs at annual, decadal, and even longer timescales. To maintain long-term productivity of the rangelands of northern Australia under highly variable rainfall conditions, suitable land management practices are assessed using rangeland biophysical models, e.g., GRASP (GRASs Production). The daily maxima of the 15 min rainfall intensity (I15) are used to predict runoff and moisture retention in the model. The performance of rangeland biophysical models heavily relies on the I15 estimates. As the number of pluviograph stations is very limited in northern Australian rangelands, an empirical I15 model (Fraser) was developed using readily available daily climate variables, i.e., daily rainfall total, daily diurnal temperature range, and daily minimum temperature. The aim of this study is to estimate I15 from daily rainfall totals using a well-established disaggregation scheme coupled with the Bartlett–Lewis rectangular pulse (BLRP) model. In the absence of pluviograph data, the BLRP models (RBL-E and RBL-G) were calibrated with the precipitation statistics estimated using the Integrated Multi-satellitE Retrievals for GPM (global precipitation measurement) (IMERG; 30 min, 0.1° resolution) precipitation product. The Fraser, RBL-E, and RBL-G models were assessed using 1 min pluviograph data at a single test site in Darwin. The results indicated that all three models tended to underestimate the observed I15, while a serious underestimation was observed for RBL-E and RBL-G. The underestimation by the Fraser, RBL-E, and RBL-G models consisted of 23%, 38%, and 50% on average, respectively. Furthermore, the Fraser model represented 29% of the variation in observed I15, whereas RBL-E and RBL-G represented only 7% and 11% of the variation, respectively. A comparison of RBL-E and RBL-G suggested that the difference in the spatial scales of IMERG and pluviograph data needs to be addressed to improve the performance of RBL-E and RBL-G. Overall, the findings of this study demonstrate that the BLRP model calibrated with IMERG statistics has the potential for estimating I15 for the GRASP biophysical model once the scale difference between IMERG and point rainfall data is addressed.

Investigating Near-Surface Hydrologic Connectivity in a Grass-Covered Inter-Row Area of a Hillslope Vineyard Using Field Monitoring and Numerical Simulations

The interplay of surface and shallow subsurface fluxes plays a critical role in controlling water movement in hillslope agroecosystems and impacting soil and plant health during prolonged dry periods, demonstrating a need for in-field monitoring. This study was conducted for two years (2021–2022) by combining field monitoring of the grass-covered inter-row area (passive wick lysimeter, surface runoff, and meteorological data), laboratory determination of soil hydraulic properties (SHPs), and numerical modeling with the aim to explore near-surface fluxes at the SUPREHILL Critical Zone Observatory (CZO) located on a hillslope vineyard. Additionally, sensitivity analysis for basic root water uptake (RWU) parameters was conducted. The model was evaluated (R2, RMSE, and NSE) with lysimeter (hillslope) and runoff (footslope) data, producing good agreement, but only after the inverse optimization of laboratory estimated hydraulic conductivity was conducted, demonstrating that adequate parameterization is required to capture the hydropedological response of erosion-affected soil systems. Results exhibit the dependence of runoff generation on hydraulic conductivity, rainfall, and soil moisture conditions. The data suggest different soil-rewetting scenarios based on temporal rainfall variability. Sensitivity analysis demonstrated that Leaf Area Index (LAI) was the most responsive parameter determining the RWU. The study offers an approach for the investigation of fluxes in the topsoil for similar sites and/or crops (and covers), presenting the methodology of self-constructed soil–water collection instruments.

Time Series Analysis of Temperature and Rainfall in the Savannah Region in Togo, West Africa

This study investigates the trend in monthly and annual rainfall, and minimum and maximum temperature (Tmin and Tmax) in the Savannah region of Togo. The historical data of Mango and Dapaong weather stations from 1981 to 2019 were used. A serial correlation test was applied to all time series to identify serially independent series. A Non-parametric Mann–Kendall (MK) test was applied to serially independent series. The magnitude of the trend was calculated using the Sen’s slope (SS) method. For the data influenced by serial correlation, a modified version of the Mann–Kendall test was applied. An open-source library package was developed in the R language, namely, “mkmodified”. For annual rainfall, results showed a significant increasing trend at Dapaong (p < 0.05) and a non-significant decreasing trend at Mango (p > 0.05) at 95%. There was an increasing trend in the Tmin both at Mango and Dapaong. No statistically significant trend was found at Mango (p > 0.05), while at Dapaong (p < 0.05), a significant trend was found at 95%. Simlarly, there was a statistically increasing trend in the Tmax both at Mango and Dapaong. Rainfall in Dapaong has increased (5.50 mm/year) whereas in Mango, it has decreased (−0.93 mm/year). Tmn increased by 0.04 and 0.008 °C per year in Mango and Dapaong, respectively. Tmax increased by 0.03 and 0.02 °C per year in Mango and Dapaong, respectively. A Rainfall Anomaly Index (RAI) was also used to present a temporal variation in rainfall; the historical series presented drier years. Many studies have analysed the trend of climate parameters in northern Togo, but none of them has specifically targeted the Savannah region that is considered the poorest region of the country.

Relevance of climatological information on spatial and temporal variability of Indian Summer monsoon rainfall (ISMR) in recent El Niño years and its impact on four important kharif crops over India

The spatial and temporal variability of Indian Summer Monsoon Rainfall (ISMR) during El Niño years (2002, 2004 & 2009) and its impact on district-wise yield of four important kharif crops viz., rice, maize, pearlmillet and sorghum over India were analysed. The number of districts fall under different rainfall classes are different in normal and El Niño years and found that there is a small shifting of spine curve towards left, which indicate the number of districts under lower rainfall classes increased in El Niño years. As far as National level analysis indicated that the yield of these major kharif crops grown steadily during the study period with higher rate of 43 kg/ha in maize and lower rate of 6.2 kg/ha in sorghum due to technological advancement. Identified high and medium vulnerable districts with respect to above crops in El Niño years based on yield changes > −10 per cent compared to average of previous and succeeding years. 77 and 36 districts identified as highly vulnerable with respect to rice and sorghum, respectively. Whereas 36 and 65 districts in the case of sorghum and maize, respectively. Thus, the results of this study provide an opportunity to policy makers/ researchers/other stakeholders to prepare short and long term contingency plan to cope up with the situation, if there is any forecast of El Niño in future.

Sensitivity of cloud microphysics on the simulation of heavy rainfall in WRF- a case study for the 7–10 August 2019 event over Kerala, India

This work investigates the mechanism of the extremely heavy rainfall event during 7–10 August 2019 in Kerala. As the skill of WRF model for simulating the heavy rainfall strongly depends on the choice of cloud microphysics (CMP), sensitivity analysis is performed using cloud resolving WRF simulations with different CMP schemes to assess their performance. Total five sensitivity experiments are conducted by varying CMP schemes namely Thompson, Goddard, WSM6, Kessler and Purdue-Lin. Model results along with the fifth generation European Reanalysis (ERA5) data indicate that the heavy rainfall occurred in association with the monsoon depression, intensification of off-shore trough, and low-level jet (LLJ) phenomena and associated moisture transport and convergence. Our results clearly suggest that the heavy rainfall occurred during 7–10 August 2019 was less intensive compared to the August 2018 event. One of the prime reasons for meagre intensity is the presence of the upper tropospheric cyclonic flow over southeast Arabian Sea off Kerala, which reduced the intensity of low-level westerly flow, moisture transport and convection in 2019 event relative to the 2018 event. Comparison of simulated rainfall against station and gridded rainfall data indicate that the spatial and temporal variations of rainfall could be reproduced except that the model slightly over-estimated rainfall at a few stations in the central parts of Kerala. Out of the five CMP schemes, Thompson scheme simulates large amounts of cloud, rain, and snow during the period of peak rainfall with relatively low amounts of ice. Moreover, Thompson indicates a high warming in the 4–6 km layer due to large snow production while Lin and Goddard configurations exhibit altitude shift in this pattern i.e., more heating in the upper troposphere (9–15 km layer) due to producing more solid hydrometeor distributions and associated freezing processes. Among the CMP schemes, Thompson followed by Goddard better simulated the spatial and temporal distribution of rainfall as they produced the strongest convergence, CAPE and vertical motions compared to WSM6, Lin and Kessler schemes. Moreover, the simulated hydrometeor mixing ratios in the 2019 heavy rainfall event are less by 60–80% as compared to that of 2018 due to differences in the synoptic settings.

Long-Term Trend and Variability of Rainfall in Bangladesh

The study of long-term rainfall data is significantly important for the country, whose food security and economy depend on the timely availability of water. In this study, spatial and temporal rainfall variability in Bangladesh were analysed for seasonal and annual trends by using monthly rainfall data for 40 years (1981–2020) from 34 stations of Bangladesh Meteorological Department. Linear regression analysis and Mann-Kendall (MK) test were used to identify trends and magnitude of the change and Sen’s slope to estimate the slope of the trend line. The Coefficient of Variation (CV) was used to examine the variability of rainfall. Spatial interpolation technique was done by a Kriging method using Surfer for interpolating the spatial pattern over Bangladesh. With the exception of a few stations, the findings of both parametric and non-parametric tests and trend tests were insignificant for most stations, and these stations revealed a falling trend in annual rainfall. Sen's test showed a decreasing trend for both annual and monsoon rainfall in almost all stations. Over the 40-year periods, among the seasons, the highest percentage of variability was observed in winter rainfall (148.6%) while the lowest percentage variability was observed in annual series (12.1%). The analysis of rainfall data could help irrigation and agricultural management plan for the most efficient use of water resources in Bangladesh.

Spatial and temporal pattern of deficient Indian summer monsoon rainfall (ISMR): impact on Kharif (summer monsoon) food grain production in India.

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4years) and 4 to 5 consecutive drought years (once in 120years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R2 (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R2 (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.

Weather Shocks, Birth and Early Life Health: Evidence of Different Gender Impacts

Abstract This paper examines the impact of exposure to weather events during gestation on birth weight and anthropometric health of a cohort of children. We explore birth records for the cohort of children born between 2003 and 2013 in Sierra Leone using Demographic Health Surveys linked to temporal variation of rainfall and temperature patterns. We find that in utero droughts (or abnormally low precipitation levels) increase the prevalence of low birth weight with larger effects among boys. However, the effects of those same in utero shocks on the prevalence of stunting up to 59 months later are smaller for boys than for girls. The gender difference in estimated impacts from birth to anthropometric health is attributed to food consumption patterns that favour boys. Our results have policy implications for tracking health outcomes during early childhood using birth and anthropometric health, especially by gender.

Numerical Evaluation of Slope Stability based on Temporal Variation of Hydraulic Conductivity

Slope failure is a common phenomenon all over the world on both man-made and natural slopes. Prolonged rainfall is one of the climatic factors which is largely responsible for slope failure. During heavy and prolonged rainfall, a part of the rainwater infiltrates through the soil and seeps into the slope. The infiltrated water lowers the matric suction and increases the porewater pressure. Eventually, the generated porewater pressure decreases the strength of the soil which results in slope failures. To evaluate the effect of rainwater seepage on slope stability, it is necessary to investigate the hydraulic conductivity of the slope soil. The objective of this study is to evaluate the effect of hydraulic conductivity on slope failure mechanisms. A finite element analysis of slope stability was conducted using Geo-Studio software. A numerical model was developed and calibrated with field monitoring data. The field monitoring data included the observation of hydraulic conductivity using a Guelph Permeameter. Afterward, the temporal variation of rainfall and hydraulic conductivity was incorporated into the SEEP/W program and the consequent changes in slope stability were evaluated in SLOPE/W. From the numerical analysis, with the identical strength parameters of the soil, different factors of safety were observed when the slope sections retain different hydraulic properties. Based on the numerical analysis, it was observed that hydraulic conductivity greater than 4×10-6 cm/s leads to slope failure. Periodic monitoring of hydraulic conductivity in the field may provide deep insight into rainfall-induced slope failures.

Densidade e classificação da rede pluviométrica e análise espaço-temporal das chuvas na região do alto Rio Paraná, Brasil

ABSTRACT Water management aims to ensure the water availability necessary to meet the current and future demand for water resources. For which it is essential to implement monitoring networks that support the investigation of events that interfere with the rainfall regime of watersheds, such the phases of the ocean-atmospheric phenomenon El Niño – Southern Oscillation (ENSO). The classification of the rainfall network was proposed according to the World Meteorological Organization (WMO) for 14 management watersheds in the Upper Parana River (UPR) region, Brazil. On the other hand, the spatial and temporal variability of annual rainfall was analyzed using geostatistical techniques and confronted with ENSO data. To this purpose, data from 408 stations were collected via the Hidroweb portal, for the period from 1990 to 2020. The low representativeness of data in the region was verified from the observational network. The areas with the lowest and highest rainfall reduction were the north and northwest regions of the UPR, and the areas surrounding the Paraná River, respectively. The years 2019 and 2020 were identified as the most critical period of the last 3 decades with below-average rainfall (-13.21%) in 49.55% of the studied area, indicating a persistence in the drought scenario.

Existential threats to pastoralism in an arid environment: the fate of Gujii pastoralists in Southern Ethiopia

&lt;span lang="EN-GB"&gt;The pastoralists of Gujii have faced numerous risks, with their pastoralism system facing serious challenges. With the technically inconsistent and timely unmanaged interventions, several pastoral households are facing unreserved challenges. As a result, the future of pastoralists appears to be in great danger. The objective of this study is to investigate the ongoing challenges of Gujii pastoralism, as well as the fate of pastoralists in southern Ethiopia who are suffering from unrelenting drought. Ethiopian Meteorological Agency provided monthly rainfall and temperature data from 1986 to 2016. A household survey was conducted with 340 household heads randomly chosen from the study area to examine the challenges of the pastoralists have been facing. Moreover, a key informant interview and a focus group discussion were employed to collect qualitative data. Each watershed unit held one round of focus group discussions, with an average of 7-10 participants. The coefficient of variation (CV) was used to detect the temporal variability of seasonal rainfall and temperature. To detect trends, the Mann Kendall (MK) and Sen's slope estimator were used to determine the trends and magnitude of the change. Political and tenure insecurity seriously affected the pastoral system in different ways. Autumn and spring had high variability in rainfall, with CV = 48.76% and 32.43%, respectively. Both winter and spring temperatures show a significant increase at (a = 0.1). Climate variability has made the already fragile pastoral households even more vulnerable. Pastoralist’s future would be difficult with all of these problems, and its resilience would be doubtful. As a result, in order to ensure the pastoral's long-term survivability, the government.&lt;/span&gt;

Analysis of Rainfall Trends in India, Incorporating Non-Parametric Tests and Wavelet Synopsis over the Last 117 Years

Rainfall trend analysis is very important in the economic and sociocultural context of the country, like the development of irrigation and water resource management practices. In this paper, Mann-Kendall (M-K), Sen’s slope estimator, and wavelet analysis were used to analyze long-term (1901 ~ 2017) annual and seasonal rainfall trends in India. The sequential Mann-Kendall (SQMK) test is used to estimate the temporal variation of rainfall. The analysis shows monsoon precipitation in “Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Uttar Pradesh, Chhattisgarh, Sikkim, Arunachal Pradesh, Mizoram, Assam, Karnataka, Nagaland, Kerala, Meghalaya, and Goa” have significant trends. The above-mentioned states were subjected to a discrete wavelet transform (DWT) analysis using Daubechies (db5-db10) and Symlet (sym5-sym10) mother wavelet families. The analysis revealed that the trends of subdivisions had a short-term periodicity of less than a decade. Therefore, the present study indicates significant changes over time. By utilizing wavelet techniques, it is possible to gain insight into both short- and long-term tendencies, making it simpler to spot patterns and sudden changes. Therefore, the present study by combining non-parametric tests with wavelet synopsis, offers a thorough comprehension of rainfall trends in India. The collaborative use of these techniques improves the precision of trend identification, offering valuable insights to make well-informed decisions for water resource management and climate adaptation strategies.

Spatial and temporal rainfall variability in the Caribbean coast of Costa Rica

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country’s economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes.

An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia

This paper presents an analysis of the temporary variation of the area-orientated annual maximum daily rainfall (AMDR) with respect to the three spatial properties: location, size and shape of the region-of-interest (ROI) in Great Britain and Australia using two century-long datasets. The Maximum Likelihood and Bayesian Markov-Chain-Monte-Carlo methods are employed to quantify the time-varying frequency of AMDR, where a large proportion of the ROIs shows a non-decreasing level of most frequent AMDR. While the most frequent AMDR values generally decrease with larger-sized ROIs, their temporal variation that can be attributed to the climate change impact does not show the same dependency on the size. Climate change impact on ROI-orientated extreme rainfall is seen higher for rounded shapes although the ROI shape is not as significant as the other two spatial properties. Comparison of the AMDR at different return levels shows an underestimation by conventionally used stationary models in regions where a nonstationary (i.e., time-varying) model is preferred. The findings suggest an overhaul of the current storm design procedure in view of the impact of not only climate change but also spatial variation in natural processes.

Modeling the spatial and temporal availability of water resources potential over Abbay river basin, Ethiopia

Study regionAbbay River Basin, Ethiopia. Study focusThis research focused on assessing the spatio-temporal availability and variability of water resources potential using physically based hydrological model. The availability and spatial variability of water balance components were modeled for each subbasins of Abbay River basin. New hydrological insights for the regionMulti statistical performance criteria proved that ArcSWAT model has the capacity to reproduce flows that agree with the measured flows at daily time scale simulation and found as applicable over Abbay River basin. The Calibration and Validation were done at different subbasins. The result revealed that the performance of the model well improved as the basin was partitioned into subbasins. For reproduction of the measured flow at different flow segments, the model performed very good especially for high and peak flows segments. The rainfall distribution revealed that the basin is characterized by high spatial and temporal variation of rainfall. Three fourth of the annual rainfall was contributed from June to September. The water balance components analysis proved that Evapotranspiration is high over the basin which covers more than half (55.20 %) of the annual rainfall. Water yield (WYLD_Qmm) covers only 21.73 % of the annual rainfall. This research can be used as basis to give emphasis on water resources potential modeling in the future considering climate and land use changes.

Performance Evaluation of Google Earth Engine Based Precipitation Datasets Under Different Climatic Zones over India

Satellite as well as reanalysis-based datasets are widely available and useful in detecting spatial and temporal variability of rainfall at a finer resolution. These products have been widely used in weather forecasting and hydrological and climate studies. However, the accuracy of satellite products varies spatially and across different datasets. In this study, the accuracy of five satellite-based precipitation products with different spatial resolutions, i.e., CHIRPS, ERA5, TRMM, GPM, and TerraClim available on Google Earth Engine (GEE) were compared with India Meteorological Department (IMD) gridded data in six climate zones in India. The statistics such as RMSE, R2, MSE, and PBIAS were computed. It was observed that the performance of each product varies in different climatic zones. The GPM was observed to have high accuracy in arid, semi-arid, and tropical wet zones. TRMM showed a good match in tropical wet and dry, tropical wet, and semi-arid zones. TerraClim and ERA5 showed high accuracy in humid subtropical and montane regions, respectively. It was also observed that CHRIPS was found to be least suitable in all the climate zones across India. The findings from the present studies will serve as a guiding document for the researcher to select appropriate datasets for different applications such as drought monitoring, precipitation anomaly, hydrological models, or other related studies in India.